What is the Impact of Poor Data Quality?

The Causes of Poor Data Quality

There are several factors that can lead to poor quality business data.

Not following company data guidelines. Breakdowns in established procedures and the failure to follow processes … all contribute to poor quality data.

Reliance on multiple systems that don’t properly integrate leads to multiple versions of the truth. Re-keying the information many times means the data is open to human error, especially if there is no data validation.

Inadequate systems integration either within the business or as a result of acquisitions and mergers. Consolidating data from multiple systems into a single platform can be a total nightmare – unless you use the services of data integration specialists - this is especially true when the data is inconsistent, inaccurate, out-of-date and duplicated in the first place.

Data decay is the process of good quality data becoming poor quality data – the data becomes outdated and the contact is uncontactable.

The Consequences of Poor Data Quality

As cited in a recent Gartner study “Twelve ways to improve your data quality” December 2015. Much of the thousands and for large corporates millions of pounds lost each year is down to lost productivity due to poor data quality.

When we have to compensate for inaccuracies and need to work around deciding how to deal with poor data quality – that’s a loss in productivity. Trustworthy data is critical for competitiveness, operational efficiency, risk reduction, customer satisfaction and protection.

How to prevent Poor Data Quality

Many companies have a mix of disparate or poorly integrated legacy applications. This leads to inconsistent and fractured processes, or multiple versions of the truth. It makes sense to be more “proactive” on improving the data quality process, instead of being “reactive.”

Poor quality data from disparate systems can be aggregated into a database, a data warehouse or a data mart.

A customer relationship bespoke database is designed to help managers make strategic decisions about their business, based on their customers and prospects. Where a data warehouse combines databases across an entire enterprise, data marts are usually smaller and focus on a particular subject or department.

Which means, unless CIOs, chief data officers and information leaders get this right by pragmatically improving their data quality, they will be unable to take full advantage of new big data opportunities such as data insights and marketing analytics.

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